PRS: Practicing Research Skills

Computational Approaches

in Political and Social

Sciences

Week 3A: Data Analysis / Skills tutorial on network analysis. Mon 17 June

Diliara Valeeva and Eelke Heemskerk

Plan for Week 3

Mon 17 June: Advanced Gephi Tutorial

                     + How to write 'Data Analysis'

Wed 19 June: Group meetups

Fri 21 June: Group meetups

=> Submitting 'Data Analysis' assignment

Plan for today

1. Reflections

2. Skills tutorial on network analysis - 2

3. How to write 'Data Analysis'

1. Reflections

2. Skills tutorial on

network analysis - 2

Image source:  https://medium.com/the-xplane-collection/anatomy-of-a-social-network-de73f1224ac5

Data on

co-authorship (2015)

Canvas => Modules => Week 3 => 2015_coauthorship.gexf

Network-related

Node-related

1. Network Measures

Average Degree

• Degree is the number of ties a node has to other nodes
• High degree: nodes have a large number of ties with others in a network 

Graph Density

• Density indicates how densely nodes are connected in a network
• = Number of actual ties / Number of potential ties
• High density: everyone knows each other in a network

Network Diameter

• Diameter shows how far apart are the most distant nodes  from each other
• High diameter: nodes are far away, it can take time and effort to reach each other

Betweenness Centrality

• Betwenness show whether a node obtains "a bridge" positions between others
• High betweenness: node has access to diverse information and resources, connects non-connected groups

Closeness Centrality

• Closeness shows how close is the node to all others
• High closeness: a node can reach others in a network very easily

Modularity

• Shows the network communities
• Nodes are in one community if they are densely connected with each other
• High modularity: dense intra-community, sparse inter-community ties
• Higher is better

Connected components

• Nodes are in one component if they are connected only with each other
• A large number of components: network is highly disconnected

2. Node Measures

Clustering coefficient

• It is a measure of how complete the neighborhood of a node is
• The friend of my friend is also my friend" effect
• High clustering: everyone knows each other

Eigenvector Centrality

• Shows how a node is connected with other influential nodes
• High eigenvector: the node has a large number of ties and its neighbors also have a large number of ties

Where to find all the results?

• Gephi returns reports
• Network-related measures are also saved in 'Data Laboratory' tab.
• Export results using "Export Table" and explore in Excel

3. Data Analysis

Data Analysis

1. Introduction

2. Description and interpretation of the results

3. Conclusions

* max 2000 words

Deadline: Friday 21 June, 19.00

1. Introduction

• Research question(s) and hypothesis(es):   A brief reminder

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• Reformulate if they have changed

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• ​Data and methods: Which dataset(s) and method(s) you used?

2. Results

• What are your main findings?​​

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• How would you explain these findings? What do they mean?​ Did you obtain what you expect or not?

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* Think about the research question

* Return to the literature review if needed

3. Conclusions

• A summary of the main findings

• Did you answer your research question or not? Did you confirm your hypothesis(es) or not?

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• What would you recommend for future studies? What can be explored more? What would you like to study more on this topic if you would have more time / better datasets?

Group meetups

Wednesday 19 June

Success offline : 9.00 - 9.20

Success online : 9.20 - 9.40

Past and Present: 9.40 - 10.00

Neighbors: 10.10 - 10.30

Key players : 10.30 - 10.50